Unsupervised metric learning
has been mainly addressed in two different directions, instance discrimination [1]}, [2]}, [3]}, [4]} and pseudo labeling [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}.
Following the contrastive learning strategy [12]}, [13]}, [14]}, [15]}, instance discrimination methods assign a unique label per training instance and learn embedding spaces where different instances are well discriminated.
Unfortunately, they have a trouble modeling variations within each latent class.
On the other hand, pseudo labeling methods discover pseudo classes by applying off-the-shelf algorithms like \(k\) -means clustering [5]}, [6]}, [7]}, [11]}, [9]}, hierarchical clustering [8]} and random walk [10]} to unlabeled training data.
Such methods can consider the class-equivalence relations between training data by utilizing existing supervised metric learning losses [23]}, [24]}, [25]} with pseudo labels.
However, the major drawback of these methods is their prohibitively high complexity imposed by the auxiliary algorithms, which obstructs end-to-end training.
